Deep Speaker: speaker recognition system

Data Set: LibriSpeech
Reference paper: Deep Speaker: an End-to-End Neural Speaker Embedding System
Reference code : https://github.com/philipperemy/deep-speaker (Thanks to Philippe Rémy)

This code was trained on librispeech-train-clean dataset, tested on librispeech-test-clean dataset. In my code, librispeech dataset shows ~5% EER with CNN model.

About the Code

train.py
This is the main file, contains training, evaluation and save-model function
models.py
The neural network used for the experiment. This file contains three models, CNN model (same with the paper’s CNN), GRU model (same with the paper's GRU), simple_cnn model. simple_cnn model has similar performance with the original CNN model, but the number of trained parameter dropped from 24M to 7M.
select_batch.py
Choose the optimal batch feed to the network. This is one of the cores of this experiment.
triplet_loss.py
This is a code to calculate triplet-loss for network training. Implementation is the same as paper.
test_model.py
This is a code that evaluates (test) the model, in terms of EER...
eval_matrics.py
For calculating equal error rate, f-measure, accuracy, and other metrics
pretaining.py
This is for pre-training on softmax classification loss.
pre_process.py
Load the utterance, filter out the mute, extract the fbank feature and save the module in .npy format.

Experimental Results

This code was trained on librispeech-train-clean dataset, tested on librispeech-test-clean dataset. In my code, librispeech dataset shows ~5% EER with CNN model.

More Details

If you want to know more details, please read deep_speaker_report.pdf (English) or deep_speaker实验报告.pdf (中文).

Simple Use

1. Preprare data.
   I provide the sample data in audio/LibriSpeechSamples/ or you can download full LibriSpeech data or prepare your own data.

2. Preprocessing.
   Extract feature and preprocessing: python preprocess.py.

3. Training.
   If you want to train your model with Triplet Loss: python train.py.
   If you want to pretrain with softmax loss first: python pretraining.py then python train.py.
   Note: If you want to pretrain or not, you need to set PRE_TRAIN(in constants.py) flag with True or False.

4. Evaluation.
   Evaluate the model in terms of EER: test_model.py.
   Note: During training, train.py also evaluates the model.

5. Plot loss curve.
   Plot loss curve and EER curve with utils.py.

import constants as c
from utils import plot_loss
loss_file=c.CHECKPOINT_FOLDER+'/losses.txt' # loss file path
plot_loss(loss_file)